{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eval import *\n",
    "# from utils import *\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(path_real, path_fake, real_sep=';', fake_sep=',', drop_columns=None):\n",
    "    real = pd.read_csv(path_real, sep=real_sep, low_memory=False)\n",
    "    fake = pd.read_csv(path_fake, sep=fake_sep, low_memory=False)\n",
    "    shared_min = min(len(fake), len(real))\n",
    "    real = real.sample(shared_min)\n",
    "    fake = fake.sample(shared_min)\n",
    "    if set(fake.columns.tolist()).issubset(set(real.columns.tolist())):\n",
    "        real = real[fake.columns]\n",
    "    elif drop_columns is not None:\n",
    "        real = real.drop(drop_columns, axis=1)\n",
    "        try:\n",
    "            fake = fake.drop(drop_columns, axis=1)\n",
    "        except:\n",
    "            print(f'Some of {drop_columns} were not found on real.index.')\n",
    "        assert len(fake.columns.tolist()) == len(real.columns.tolist()), f'Real and fake do not have same nr of columns: {len(fake.columns)} and {len(real.columns)}'\n",
    "        fake.columns = real.columns\n",
    "    else:\n",
    "        fake.columns = real.columns\n",
    "        \n",
    "    for col in fake.columns:\n",
    "        fake[col] = fake[col].astype(real[col].dtype)\n",
    "    return real, fake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def plot_em_all(real, fake):\n",
    "    if not isinstance(fake, list):\n",
    "        fake = [fake]\n",
    "    for x in fake:\n",
    "        if isinstance(x, dict):\n",
    "            for key, value in x.items():\n",
    "                plot_corr_diff(real[key], fake[key], plot_diff=True)\n",
    "                plt.show()\n",
    "        else:\n",
    "            assert isinstance(x, pd.DataFrame)\n",
    "            plot_corr_diff(real, x, plot_diff=True)\n",
    "            plt.show()\n",
    "\n",
    "def plot_stats(real, fakes, is_dict=False, which='cat'):\n",
    "    if not isinstance(fakes, list):\n",
    "        fakes = [fakes]\n",
    "    \n",
    "    for fake in fakes:\n",
    "        if is_dict:\n",
    "            real_df, fake_df = real[which], fake[which]\n",
    "        fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "        real_num_mean = np.log10(np.add(real_df.mean().values, 1e-5))\n",
    "        fake_num_mean = np.log10(np.add(fake_df.mean().values, 1e-5))\n",
    "        sns.scatterplot(x=real_num_mean, \n",
    "                        y=fake_num_mean, \n",
    "                        ax=ax[0])\n",
    "        line = np.arange(min(real_num_mean + [-5]), max(real_num_mean + [10]))\n",
    "        sns.lineplot(x=line, y=line, ax=ax[0])\n",
    "        ax[0].set_title('Means of real and fake data')\n",
    "        ax[0].set_xlabel('real data mean (log)')\n",
    "        ax[0].set_ylabel('fake data mean (log)')\n",
    "\n",
    "        real_cat_std = np.log10(np.add(real_df.std().values, 1e-5))\n",
    "        fake_cat_std = np.log10(np.add(fake_df.std().values, 1e-5))\n",
    "        line = np.arange(min(real_cat_std + [-5]), max(real_cat_std + [10]))\n",
    "        sns.scatterplot(x=real_cat_std, \n",
    "                        y=fake_cat_std, \n",
    "                        ax=ax[1])\n",
    "        sns.lineplot(x=line, y=line, ax=ax[1])\n",
    "        ax[1].set_title('Stds of real and fake data')\n",
    "        ax[1].set_xlabel('real data std (log)')\n",
    "        ax[1].set_ylabel('fake data std (log)')\n",
    "        plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Berka"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original TGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/berka/berka_cat.csv', '../final_data/berka/berka_sample_tgan.csv')\n",
    "tgan_org_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgan_org_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgan_org_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## WGAN-GP Fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/berka/berka_cat.csv', '../final_data/berka/berka_sample_tgan-wgan-gp.csv')\n",
    "wgan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wgan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wgan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TGAN Skip Connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/berka/berka_cat.csv', '../final_data/berka/berka_sample_tgan-skip-connections.csv')\n",
    "skip_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## MedGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/berka/berka_cat.csv', '../final_data/berka/sample_berka_medgan_100.csv')\n",
    "medgan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "medgan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "medgan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TableGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/berka/berka_cat.csv', '../final_data/berka/sample_berka_tablegan_100.csv')\n",
    "tablegan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tablegan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tablegan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_col = 'trans_type'\n",
    "x = numerical_encoding(fake.drop([target_col], axis=1), nominal_columns=tgan_org_evaluator.categorical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.15558081e-03 2.25478515e-03 1.00895154e-02 1.60792182e-03 3.68700434e-03 0.00000000e+00 4.03186594e-05 0.00000000e+00 0.00000000e+00 3.42898353e-05 2.31575622e-04 0.00000000e+00 6.94454588e-04 2.26483782e-02\n",
      " 1.71159461e-03 2.68261433e-03 5.46665979e-01 2.46056113e-03 4.02035426e-01]\n",
      "Index(['trans_id', 'account_id', 'trans_amount', 'balance_after_trans',\n",
      "       'trans_operation_CC_WITHDRAWAL',\n",
      "       'trans_operation_COLLECTION_FROM_OTHER_BANK',\n",
      "       'trans_operation_CREDIT_IN_CASH',\n",
      "       'trans_operation_REMITTANCE_TO_OTHER_BANK', 'trans_operation_UNKNOWN',\n",
      "       'trans_operation_WITHDRAWAL_IN_CASH', 'trans_k_symbol_HOUSEHOLD',\n",
      "       'trans_k_symbol_INSURANCE_PAYMENT', 'trans_k_symbol_INTEREST_CREDITED',\n",
      "       'trans_k_symbol_LOAN_PAYMENT', 'trans_k_symbol_OLD_AGE_PENSION',\n",
      "       'trans_k_symbol_PAYMENT_FOR_STATEMENT',\n",
      "       'trans_k_symbol_SANCTION_INTEREST', 'trans_k_symbol_UNKNOWN',\n",
      "       'trans_date'],\n",
      "      dtype='object')\n",
      "[ 5  7  8 11  9  6 10 12  3 14  1 17 15  0  4  2 13 18 16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = tgan_org_evaluator.f_classifiers[2].feature_importances_\n",
    "print(feature_importance)\n",
    "# feature_importance = clf.feature_importances_\n",
    "# make importances relative to max importance\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "column_names = x.columns\n",
    "print(column_names)\n",
    "print(sorted_idx)\n",
    "plt.yticks(pos, column_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = tgan_org_evaluator.r_classifiers[2].feature_importances_\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "column_names = x.columns\n",
    "plt.yticks(pos, column_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Creditcard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original TGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/creditcard/creditcard_num.csv', '../final_data/creditcard/creditcard_sample_tgan.csv', fake_sep=';')\n",
    "tgan_org_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgan_org_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgan_org_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## WGAN-GP Fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/creditcard/creditcard_num.csv', '../final_data/creditcard/creditcard_sample_tgan-wgan-gp.csv', fake_sep=';')\n",
    "wgan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wgan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wgan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TGAN Skip Connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/creditcard/creditcard_num.csv', '../final_data/creditcard/creditcard_sample_tgan-skip-connections.csv', fake_sep=';')\n",
    "skip_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## MedGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/creditcard/creditcard_num.csv', '../final_data/creditcard/sample_creditcard_medgan_100.csv', fake_sep=';')\n",
    "medgan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "medgan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "medgan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TableGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/creditcard/creditcard_num.csv', '../final_data/creditcard/sample_creditcard_tablegan_100.csv', fake_sep=';')\n",
    "tablegan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tablegan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tablegan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_col = 'trans_type'\n",
    "x = numerical_encoding(fake.drop([target_col], axis=1), nominal_columns=tgan_org_evaluator.categorical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.15558081e-03 2.25478515e-03 1.00895154e-02 1.60792182e-03 3.68700434e-03 0.00000000e+00 4.03186594e-05 0.00000000e+00 0.00000000e+00 3.42898353e-05 2.31575622e-04 0.00000000e+00 6.94454588e-04 2.26483782e-02\n",
      " 1.71159461e-03 2.68261433e-03 5.46665979e-01 2.46056113e-03 4.02035426e-01]\n",
      "Index(['trans_id', 'account_id', 'trans_amount', 'balance_after_trans',\n",
      "       'trans_operation_CC_WITHDRAWAL',\n",
      "       'trans_operation_COLLECTION_FROM_OTHER_BANK',\n",
      "       'trans_operation_CREDIT_IN_CASH',\n",
      "       'trans_operation_REMITTANCE_TO_OTHER_BANK', 'trans_operation_UNKNOWN',\n",
      "       'trans_operation_WITHDRAWAL_IN_CASH', 'trans_k_symbol_HOUSEHOLD',\n",
      "       'trans_k_symbol_INSURANCE_PAYMENT', 'trans_k_symbol_INTEREST_CREDITED',\n",
      "       'trans_k_symbol_LOAN_PAYMENT', 'trans_k_symbol_OLD_AGE_PENSION',\n",
      "       'trans_k_symbol_PAYMENT_FOR_STATEMENT',\n",
      "       'trans_k_symbol_SANCTION_INTEREST', 'trans_k_symbol_UNKNOWN',\n",
      "       'trans_date'],\n",
      "      dtype='object')\n",
      "[ 5  7  8 11  9  6 10 12  3 14  1 17 15  0  4  2 13 18 16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = tgan_org_evaluator.f_classifiers[2].feature_importances_\n",
    "print(feature_importance)\n",
    "# feature_importance = clf.feature_importances_\n",
    "# make importances relative to max importance\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "column_names = x.columns\n",
    "print(column_names)\n",
    "print(sorted_idx)\n",
    "plt.yticks(pos, column_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = tgan_org_evaluator.r_classifiers[2].feature_importances_\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "column_names = x.columns\n",
    "plt.yticks(pos, column_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Census"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original TGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/census/census_cat.csv', '../final_data/census/census_sample_tgan.csv', real_sep=',')\n",
    "tgan_org_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgan_org_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tgan_org_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## WGAN-GP Fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/census/census_cat.csv', '../final_data/census/census_sample_tgan-wgan-gp.csv', real_sep=',')\n",
    "wgan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wgan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wgan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TGAN Skip Connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/census/census_cat.csv', '../final_data/census/census_sample_tgan-skip-connections-columns-corrected.csv', real_sep=',', fake_sep=';')\n",
    "skip_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## MedGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/census/census_cat.csv', '../final_data/census/sample_census_medgan_100.csv', real_sep=',')\n",
    "medgan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "medgan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "medgan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TableGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "real, fake = get_data('../data/census/census_cat.csv', '../final_data/census/sample_census_tablegan_100.csv', real_sep=',')\n",
    "tablegan_evaluator = DataEvaluator(real, fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tablegan_evaluator.evaluate(target_col='trans_type', verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tablegan_evaluator.visual_evaluation(annot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_col = 'trans_type'\n",
    "x = numerical_encoding(fake.drop([target_col], axis=1), nominal_columns=tgan_org_evaluator.categorical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.15558081e-03 2.25478515e-03 1.00895154e-02 1.60792182e-03 3.68700434e-03 0.00000000e+00 4.03186594e-05 0.00000000e+00 0.00000000e+00 3.42898353e-05 2.31575622e-04 0.00000000e+00 6.94454588e-04 2.26483782e-02\n",
      " 1.71159461e-03 2.68261433e-03 5.46665979e-01 2.46056113e-03 4.02035426e-01]\n",
      "Index(['trans_id', 'account_id', 'trans_amount', 'balance_after_trans',\n",
      "       'trans_operation_CC_WITHDRAWAL',\n",
      "       'trans_operation_COLLECTION_FROM_OTHER_BANK',\n",
      "       'trans_operation_CREDIT_IN_CASH',\n",
      "       'trans_operation_REMITTANCE_TO_OTHER_BANK', 'trans_operation_UNKNOWN',\n",
      "       'trans_operation_WITHDRAWAL_IN_CASH', 'trans_k_symbol_HOUSEHOLD',\n",
      "       'trans_k_symbol_INSURANCE_PAYMENT', 'trans_k_symbol_INTEREST_CREDITED',\n",
      "       'trans_k_symbol_LOAN_PAYMENT', 'trans_k_symbol_OLD_AGE_PENSION',\n",
      "       'trans_k_symbol_PAYMENT_FOR_STATEMENT',\n",
      "       'trans_k_symbol_SANCTION_INTEREST', 'trans_k_symbol_UNKNOWN',\n",
      "       'trans_date'],\n",
      "      dtype='object')\n",
      "[ 5  7  8 11  9  6 10 12  3 14  1 17 15  0  4  2 13 18 16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = tgan_org_evaluator.f_classifiers[2].feature_importances_\n",
    "print(feature_importance)\n",
    "# feature_importance = clf.feature_importances_\n",
    "# make importances relative to max importance\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "column_names = x.columns\n",
    "print(column_names)\n",
    "print(sorted_idx)\n",
    "plt.yticks(pos, column_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = tgan_org_evaluator.r_classifiers[2].feature_importances_\n",
    "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "sorted_idx = np.argsort(feature_importance)\n",
    "pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "column_names = x.columns\n",
    "plt.yticks(pos, column_names[sorted_idx])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Variable Importance')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fastai (3.6.7)",
   "language": "python",
   "name": "fastai"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
